This brief considers the control design problem for a flexible robotic manipulator. Our control strategies are to: 1) move the manipulator to the certain desired angle; 2) suppress the vibration at the neighborhood of the desired angle; and 3) handle the backlash nonlinearity existing in the practical system. Based on the infinite-dimensional dynamic model, a boundary controller with input backlash is designed to achieve the control aims. An output feedback method is adopted to represent a controller to handle the feedback signals that are not able to be measured directly. The effectiveness of the designed controllers and the stability of the system are demonstrated by theoretical analysis, numerical simulations, and physical experiments.

PDE Model-Based Boundary Control Design for a Flexible Robotic Manipulator With Input Backlash

Renewable energy becomes an asset to the world׳s energy resource for its eco-friendly and low cost energy production feature. As an important renewable energy source, wind turbine technology has become a significant contributor to the world energy production because of its feasible production cost, reliability and efficiency. Researchers are very active to optimize the effectiveness of wind turbines which may lead to increase the productivity of this source of energy. Vibration in the wind turbine system affects the productivity and thus reduces efficiency. Vibration of a system cannot be destroyed but can be reduced or converted to energy using appropriate strategies. Vibration control system improves structural response of wind turbines and reliability which has impact on lifetime of the components. Lowering the vibration amplitude of a system will provide a lesser amount of noise, assure user and operating comport, maintain the high performance and production efficiency. These will assist the system to prolong the lifetime of an industrial structure or machinery. Also vibration control enhances the performance of wind turbines providing suitable work environment without external disturbance. This paper presents an ample review on performance enhancement of the wind turbines by vibration mitigation. The aim of this review is to provide a concise point for researchers to assess the current trend to control vibration of wind turbines technology. This paper will focus on main vibration control techniques of wind turbine structures. It provides the applications of passive, active and semi-active and vibration control strategies for structures, especially for wind turbines. Besides, this paper reviews on damping devices needed for vibration mitigation of structures. These damping devices have been implemented extensively in wind turbines for increasing their efficiency by mitigating vibration. This paper also reviews and assesses the performance of different control policies to control the system input and power input of damping devices.

Performance enhancement of wind turbine systems with vibration control: A review

A broadband elastic wave absorption by a sub-wavelength and lightweight structure is of considerable significance in vibration suppression, especially for low frequencies in plate-like structure. However, it has always been a great challenge. In this research, we systematically study the flexural wave diffraction in a thin plate. Based on the diffraction mechanism, we propose the concept of sub-wavelength lossy gradient elastic metasurface for flexural wave absorption. We theoretically reveal high-efficiency and quasi-omnidirectional absorption behavior, which stem from maximum multireflection-enhanced absorption of the 0th order diffraction. We experimentally demonstrate a robust high-efficiency absorption in the frequency range from 343 to 1000 Hz (larger than 1.5 octaves). In addition, we propose a general approach which involves new physics of adjusting an arrangement sequence of subunits to suppress the first-order diffraction mode. This allows to further reduce the sub-wavelength thickness of the metasurface while maintaining its high-efficiency absorption. Our designs could provide new routes to broadband vibration suppression and cancelation in low frequency by lossy elastic metamaterials and metasurfaces.

https://hal.archives-ouvertes.fr/hal-03043149/document

Flexural wave absorption by lossy gradient elastic metasurface

A Brief Review on injection moulding manufacturing process

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant.

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy.

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator

The industrial requirements for controllers able to perform tasks in the presence of plant nonlinearities are growing. In addition, an increase in industrial computation power is allowing the implementation of more complex control algorithms in the fast processing industry. In this investigation three different nonlinear model predictive control algorithms are tested and evaluated in simulation and experimentally. The methodologies are adaptive nonlinear model predictive control (nMPC), PID based nMPC (PIDnMPC), and a novel simplified nMPC (SnMPC). These are tested in simulation with an inverted pendulum, a Van der Pol oscillator, and a planar 2-link vertical robotic arm. The controllers are tested experimentally using a fabricated planar 2-link vertical robotic arm apparatus. A comparison of the different algorithms is made with special attention to trajectory tracking, computational complexity and transient response dynamics. HighlightsThree non-linear MPC Algorithms are developed to control fast systems, nMPC, PIDnMPC and SnMPC.The algorithms are tested in simulation using three non-linear models.The controller features are discussed and future directions of study are suggested.

Non-linear model predictive control schemes with application on a 2 link vertical robot manipulator

Quality measurement on injection molded parts are related to process parameter data.The data experimentation data is analyzed using statistical methods (PCA), (ICA) and (ANOVA).Models are constructed to predict a quality index based on data analysis results.A MPC scheme that allows for on-line quality control was developed using the models.The presented scheme is extendable to a variety of manufacturing processes. Quality control is an important aspect of manufacturing processes. Product quality of injection molded parts is influenced by the injection molding process. In this study statistical tools were used to develop a model that relates injection molding process variables to part quality. A statistically based model predictive control algorithm was developed for controlling part quality with manipulated variables coolant flow rate and coolant temperature. This approach replaces the need of off-line quality measurement and provides real-time injection modeling quality control.

A principal component analysis model-based predictive controller for controlling part warpage in plastic injection molding

Significant growth in the application of Artificial Intelligence (AI) due to the introduction of the Industrial Internet of things (IIoT) and cloud computing is leading to the practical presence of machine learning (ML) on the factory floor. This trend is described as a fourth industrial revolution, Industry 4.0. To this end, a convenient platform for the practical inclusion of AI at the controller level of the injection molding process is outlined. Special attention is given to the adaptability of the methodology so that it can be considered for the general case. An initial test conducted on an injection molding machine (IMM) shows that data mining and ML methods can be used for industrial big data to improve controller performance.

Meaghan Charest

Papers

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator

Non-linear model predictive control schemes with application on a 2 link vertical robot manipulator

A principal component analysis model-based predictive controller for controlling part warpage in plastic injection molding

Integration of artificial intelligence in an injection molding process for on-line process parameter adjustment

MPC enhancement for tracking of complex profiles — The basic technique